{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from pylab import mpl\n",
    "plt.rcParams['font.sans-serif'] = ['STSong']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取文件列表\n",
    "file_name = os.listdir('./data')\n",
    "res = [file for file in file_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datas = []\n",
    "for file in res:\n",
    "    filename = file.replace('.csv','')\n",
    "    file = './data/'+file\n",
    "    try:\n",
    "        data = pd.read_csv(file)\n",
    "        datas.append(data)\n",
    "        # price = data['unit_price']\n",
    "        # max_price = price.max()\n",
    "        # min_price = price.min()\n",
    "        # mean_price = price.mean()\n",
    "        # median_price = price.median()\n",
    "        # print(\"%s二手房最高价格：%.2f元/平方米\" %(filename,max_price))\n",
    "        # print(\"%s二手房最低价格：%.2f元/平方米\" %(filename,min_price))\n",
    "        # print(\"%s二手房平均价格：%.2f元/平方米\" %(filename,mean_price))\n",
    "        # print(\"%s二手房中位数价格：%.2f元/平方米\" %(filename,median_price))\n",
    "    except:\n",
    "        print('%s暂无数据'%filename)\n",
    "\n",
    "# 得到所有合并数据\n",
    "result = pd.concat(datas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110556 entries, 0 to 1297\n",
      "Data columns (total 16 columns):\n",
      "title                110556 non-null object\n",
      "price                110556 non-null float64\n",
      "unit_price           110556 non-null float64\n",
      "community_name       110556 non-null object\n",
      "region               110556 non-null object\n",
      "type                 110556 non-null object\n",
      "construction_area    110556 non-null object\n",
      "orientation          108777 non-null object\n",
      "decoration           108863 non-null object\n",
      "floor                110556 non-null object\n",
      "elevator             110556 non-null object\n",
      "purposes             110556 non-null object\n",
      "release_date         110556 non-null object\n",
      "house_structure      110412 non-null object\n",
      "image_urls           110556 non-null object\n",
      "from_url             110556 non-null object\n",
      "dtypes: float64(2), object(14)\n",
      "memory usage: 14.3+ MB\n",
      "None\n",
      "(110556, 16)\n"
     ]
    }
   ],
   "source": [
    "print(result.info())\n",
    "print(result.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_profiling as ppf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37ad6a83a91b4f4ab1da9f4c8e2656d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(HTML(value='<div id=\"overview-content\" class=\"row variable spacing\">\\n    <div class=\"row\">\\n   …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Report generated with <a href=\"https://github.com/pandas-profiling/pandas-profiling\">pandas-profiling</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppf.ProfileReport(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 去重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    103494\n",
      "True       7062\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(result.duplicated().value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    103494\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "res = result.drop_duplicates(subset=None,keep='first',inplace=False)\n",
    "print(res.duplicated().value_counts()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检测与处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                   0\n",
       "price                   0\n",
       "unit_price              0\n",
       "community_name          0\n",
       "region                  0\n",
       "type                    0\n",
       "construction_area       0\n",
       "orientation          1779\n",
       "decoration           1693\n",
       "floor                   0\n",
       "elevator                0\n",
       "purposes                0\n",
       "release_date            0\n",
       "house_structure       144\n",
       "image_urls              0\n",
       "from_url                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值处理\n",
    "res.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "res1 = res.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除车库\n",
    "cart_index = res1[res1['purposes']=='车库'].index\n",
    "res1 = res1.drop(cart_index,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 填充NAN\n",
    "orientations = ['东','南','西','北','东南','西南','东北','西北']\n",
    "decorations = ['简装','精装','毛坯','其他']\n",
    "house_structures = ['钢混结构','钢结构','混合结构','框架结构','未知','砖混结构','砖木结构']\n",
    "res1['orientation'].fillna(random.choice(orientations),inplace=True)\n",
    "res1['decoration'].fillna(random.choice(decorations),inplace=True)\n",
    "res1['house_structure'].fillna(random.choice(house_structures),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检测与处理异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               price     unit_price  construction_area\n",
      "count  101249.000000  101249.000000      101249.000000\n",
      "mean      156.685416   14608.344076         110.271534\n",
      "std       173.083472    6499.169900        2615.638107\n",
      "min         0.000000       0.000000           9.530000\n",
      "25%        81.600000   10121.500000          72.480000\n",
      "50%       120.000000   13425.200000          89.280000\n",
      "75%       176.000000   17704.300000         120.170000\n",
      "max     10000.000000  135928.000000      832186.000000\n"
     ]
    }
   ],
   "source": [
    "# 异常值处理\n",
    "# parse ㎡ into null and parse into float\n",
    "res1['construction_area'] = res1['construction_area'].str.replace('㎡','').astype(\"float\")\n",
    "print(res1.describe())\n",
    "u_mean_price = res1.describe().loc['mean','unit_price']\n",
    "mean_price = res1.describe().loc['mean','price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,15)) \n",
    "plt.subplot(221)\n",
    "plt.scatter(res1[\"construction_area\"], res1[\"price\"])\n",
    "plt.xlabel('建筑面积',fontsize=15)\n",
    "plt.ylabel('总价',fontsize=15)\n",
    " \n",
    "plt.subplot(222)\n",
    "plt.scatter(res1[\"construction_area\"], res1[\"unit_price\"])\n",
    "plt.xlabel('建筑面积',fontsize=15)\n",
    "plt.ylabel('单价',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8)) \n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(res1[\"price\"])\n",
    "plt.ylabel('总价',fontsize=15)\n",
    "plt.subplot(1,2,2)\n",
    "plt.boxplot(res1[\"unit_price\"])\n",
    "plt.ylabel('单价',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 异常值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理离群值和有失一般性值\n",
    "res1.drop(res1[res1['construction_area']>1000].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,15)) \n",
    "plt.subplot(221)\n",
    "plt.scatter(res1[\"construction_area\"], res1[\"price\"])\n",
    "plt.xlabel('建筑面积',fontsize=15)\n",
    "plt.ylabel('总价',fontsize=15)\n",
    " \n",
    "plt.subplot(222)\n",
    "plt.scatter(res1[\"construction_area\"], res1[\"unit_price\"])\n",
    "plt.xlabel('建筑面积',fontsize=15)\n",
    "plt.ylabel('单价',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   title  price  unit_price community_name         region  \\\n",
      "250  标准套二   精装修     中间楼层    0.0         0.0           东洪广厦  ['锦江', '三圣乡']   \n",
      "\n",
      "       type  construction_area orientation decoration       floor elevator  \\\n",
      "250  2室1厅1卫              71.17          东南         精装  中楼层 (共34层)        有   \n",
      "\n",
      "    purposes release_date house_structure  \\\n",
      "250     普通住宅   2020-01-04            钢混结构   \n",
      "\n",
      "                                            image_urls  \\\n",
      "250  ['https://vrlab-image4.ljcdn.com/release/auto3...   \n",
      "\n",
      "                                              from_url  \n",
      "250  https://cd.ke.com/ershoufang/20010617710100167...  \n",
      "250    东洪广厦\n",
      "Name: community_name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 处理price和unit_price为 0 的数据\n",
    "print(res1[res1['unit_price']==0])\n",
    "print(res1[res1['unit_price']==0]['community_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [title, price, unit_price, community_name, region, type, construction_area, orientation, decoration, floor, elevator, purposes, release_date, house_structure, image_urls, from_url]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# def judge(community):\n",
    "#     # 获取当前小区均价\n",
    "#     com = res[res['community_name']==community]\n",
    "#     com_unit_price = com['unit_price'].mean()\n",
    "#     return com_unit_price\n",
    "\n",
    "\n",
    "# res.iloc[32354,3] = judge(str(res[res['unit_price']==0]['community_name']))\n",
    "# total_price =format(float(res.iloc[32354,7]) * u_mean_price / 10000,'.1f')\n",
    "# res.iloc[32354,2] = total_price\n",
    "\n",
    "# res.iloc[32354,3] = u_mean_price\n",
    "# total_price =format(float(res.iloc[32354,7]) * u_mean_price / 10000,'.1f')\n",
    "# res.iloc[32354,2] = total_price\n",
    "# print(res.loc[32354])\n",
    "\n",
    "# 直接删除\n",
    "res1.drop(res1[res1['price']==0].index,inplace=True)\n",
    "print(res1[res1['unit_price']==0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 其他标准"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,12))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.scatter(x=res1.decoration, y=res1.price,color='b')\n",
    "plt.xlabel(\"decoration\", fontsize=15)\n",
    "plt.ylabel(\"price\", fontsize=15)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.scatter(x=res1.house_structure, y=res1.price,color='b')\n",
    "plt.xlabel(\"house_structure\", fontsize=15)\n",
    "plt.ylabel(\"price\", fontsize=15)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.scatter(x=res1.purposes, y=res1.price,color='b')\n",
    "plt.xlabel(\"purposes\", fontsize=15)\n",
    "plt.ylabel(\"price\", fontsize=15)\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.scatter(x=res1.construction_area, y=res1.price,color='b')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"construction_area\", fontsize=15)\n",
    "plt.ylabel(\"price\", fontsize=15)    \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 删除异常值\n",
    "res1.drop(res1[(res1['decoration']=='其他') & (res1['price']>6000)].index,inplace=True)\n",
    "res1.drop(res1[(res1['house_structure']=='钢混结构') & (res1['price']>7000)].index,inplace=True)\n",
    "res1.drop(res1[(res1['house_structure']=='框架结构') & (res1['price']>6000)].index,inplace=True)\n",
    "res1.drop(res1[(res1['house_structure']=='未知结构') & (res1['price']>4000)].index,inplace=True)\n",
    "res1.drop(res1[(res1['purposes']=='普通住宅') & (res1['price']>6000)].index,inplace=True)\n",
    "res1.drop(res1[(res1['construction_area']>700) & (res1['price']<300)].index,inplace=True)\n",
    "res1.drop(res1[(res1['construction_area']<600) & (res1['price']>4000)].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 地理编码转换\n",
    "#### 使用百度地图解析进行具体地址转换为经纬度操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_data = res1.copy()\n",
    "# 添加id\n",
    "complete_data['id'] = range(len(complete_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import re\n",
    "from urllib.request import quote\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lat:30.48943665040442\n",
      "lang:103.89577048859141\n"
     ]
    }
   ],
   "source": [
    "# lat纬度 lng经度\n",
    "def getlnglat_baidu(address):\n",
    "    \"\"\"\n",
    "    获取一个中文地址的经纬度(lat:纬度值,lng:经度值)\n",
    "    \"\"\"\n",
    "    address = quote(address) # quote编码,防止中文乱码\n",
    "    url_base = \"http://api.map.baidu.com/geocoder/v2/\"\n",
    "    output = \"json\"\n",
    "    ak = \"Qmz0VMtKw3uAI2GWClu9Q6iCnP2j2uH2\"\n",
    "    url = url_base + '?' + 'address=' + address  + '&output=' + output + '&ak=' + ak\n",
    "    \n",
    "    lat = 0.0\n",
    "    lng = 0.0\n",
    "    res = requests.get(url)\n",
    "    temp = json.loads(res.text)\n",
    "    if temp[\"status\"] == 0:\n",
    "        lat = temp['result']['location']['lat']\n",
    "        lng = temp['result']['location']['lng']\n",
    "    return lat,lng\n",
    "\n",
    "def getlnglat_gaode(address):\n",
    "    address = quote(address)\n",
    "    url_base = \"http://restapi.amap.com/v3/geocode/geo\"\n",
    "    output = \"json\"\n",
    "    key = \"5d297ac38ce0db596ad9656b13fa9b08\"\n",
    "    url = url_base + '?' + 'address=' + address  + '&output=' + output + '&key=' + key\n",
    "    \n",
    "    lat = 0.0\n",
    "    lng = 0.0\n",
    "    res = requests.get(url)\n",
    "    temp = json.loads(res.text)\n",
    "    location = temp['geocodes'][0]['location'].split(',')\n",
    "    if temp[\"info\"] == 'OK':\n",
    "        lat = location[1]\n",
    "        lng = location[0]\n",
    "    return lat,lng\n",
    "\n",
    "lat,lang = getlnglat_baidu('四川省成都市新津金秋乐园一期')\n",
    "print('lat:{}'.format(lat))\n",
    "print('lang:{}'.format(lang))\n",
    "\n",
    "# lat,lang = getlnglat_gaode('四川省成都市新津金秋乐园一期')\n",
    "# print(lat)\n",
    "# print(lang)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100796 30.426557 103.795338\n",
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      "100982 30.419489 103.816134\n",
      "100983 30.432251 103.815759\n"
     ]
    }
   ],
   "source": [
    "# 生成经纬度信息\n",
    "idint = []\n",
    "community_names = []\n",
    "lats = []\n",
    "lngs = []\n",
    "address = ''\n",
    "lat_lng_data = {\"id\":idint,\"community_name\":community_names,\"lat\":lats,\"lng\":lngs}\n",
    "\n",
    "for idi,community_name,region in zip(list(complete_data[\"id\"]),list(complete_data[\"community_name\"]),list(complete_data[\"region\"])):\n",
    "    community_name = str(community_name)\n",
    "    region = re.sub(r\"\\[|\\]|'\",\"\",region).split(',')\n",
    "    if len(region)>=2:\n",
    "        if region[0] != region[1]:\n",
    "            address = \"成都市\"+region[0]+region[1]+community_name\n",
    "        else:\n",
    "            address = \"成都市\"+region[0]+community_name\n",
    "    else:\n",
    "        address = \"成都市\"+region[0]+community_name\n",
    "#     print(address)\n",
    "#     print('*'*20)\n",
    "    lat,lng = getlnglat_gaode(address)\n",
    "    if lat != 0 or lng !=0:\n",
    "        idint.append(idi)\n",
    "        community_names.append(community_name)\n",
    "        lats.append(lat)\n",
    "        lngs.append(lng)\n",
    "        print(idi,lat,lng)\n",
    "    if idi>0 and idi%2000==0:\n",
    "        df_latlng = pd.DataFrame(lat_lng_data)\n",
    "        df_latlng.to_csv(\"./cleandata/latlng\"+str(idi)+\".csv\",encoding='gbk')\n",
    "        idint = []\n",
    "        community_names = []\n",
    "        lats = []\n",
    "        lngs = []\n",
    "        address = ''\n",
    "        lat_lng_data = {\"id\":idint,\"community_name\":community_names,\"lat\":lats,\"lng\":lngs}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理剩下的数据\n",
    "df_latlng = pd.DataFrame(lat_lng_data)\n",
    "df_latlng.to_csv(\"./cleandata/latlng100983.csv\")\n",
    "\n",
    "# 生成经纬度txt文件\n",
    "# out_map = \"./cleandata/LNGLAT.txt\"\n",
    "# with open(out_map,\"w\") as file_out:\n",
    "#     for lng,lat in zip(list(df_latlng[\"lng\"]),list(df_latlng[\"lat\"])):\n",
    "#         out = str(lng)+\",\"+str(lat)\n",
    "#         file_out.write(out)\n",
    "#         file_out.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "position_name = os.listdir('./cleandata/')\n",
    "res = [position for position in position_name]\n",
    "\n",
    "datas = []\n",
    "for file in res:\n",
    "    filename = file.replace('.csv','')\n",
    "    file = './cleandata/'+file\n",
    "    try:\n",
    "        data = pd.read_csv(file,encoding='gbk')\n",
    "        datas.append(data)\n",
    "    except:\n",
    "        print('%s暂无数据'%filename)\n",
    "\n",
    "# 得到所有合并数据\n",
    "position_result = pd.concat(datas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "del position_result['Unnamed: 0']\n",
    "position_result.to_csv('./cleandata/lnglat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 合并数据，得到最终数据\n",
    "del position_result[\"community_name\"]\n",
    "df_merge = pd.merge(complete_data,position_result,on=\"id\")\n",
    "df_merge.to_csv('./housedata/fin_house.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.preprocessing import LabelEncoder                    #标签编码\n",
    "from sklearn.preprocessing import RobustScaler, StandardScaler    #去除异常值与数据标准化\n",
    "from sklearn.pipeline import Pipeline, make_pipeline              #构建管道\n",
    "from scipy.stats import skew                                 #偏度\n",
    "from scipy.special import boxcox1p                           # box-cox变换\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./housedata/fin_house2.csv',encoding='gbk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# price处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 此函数用于绘制特征变量的分布图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 155.79 and sigma = 155.26\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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DRUUFq1ev5oknnmD79u188IMf5Morr+Qb3/gGTz31FBMnTuw2BvLQoUOd/iEQFivXFGczuwJodvenzezDwNPuvqqnduA64IvunjCzfwY+B7wXSLr73fnea8GCBf7iiy+W5XMU63/99M889Vozt/6POVyz4ISB7o6ISElWrVpFY2PjQHejT1KpFNFolLa2NqLRKOl08A9pd28fDJ5KpYraMDtbbUkmk+1jy7K/F+pHP/oRH/jABzjllFNYunRptyrcYNPW1sbOnTsZP3583mO7VhCL/f5Q9eabb/LYY4/1OAu4p/+GzOwld19QyLXLOUbtfGBz5vVm4Lwc7TPdPbugTBqYCrwP+J2ZfcLMsue2M7ObzOxFM3txx44d5foMRUumguC7vvngAPdEROTYln08WVFRQTQaJR6PE4/HqaioIBKJEIlEigppcGTWYiwWa79Gsa677jp+9KMfEYlEBn1Ig+D+FRLSIP+WSUdjSEulUtx///285z3vKcv1yxnULPMry3O0d+2HA+7uO4EfAB/venF3v93dF7j7gvr6+vB6XaJk5l9s67YfGOCeiIjIYFRTU8ONN97Y4+B1GXoOHz7MjTfe2KfQXohyBrXFwJTM6wbgqRztq80sOwLPgPVAtiQVAUrber4fJVRRExGRPIYPH95e8ZOhraampujKbDHKGdQeAMaZ2ZnANuAGMzu3a7u7rwJuBd5pZguB+9w9CdxuZu8D3g18vYz9DFW2orZx58H2iQUiIiIifVG2WZ8ezFJY1KHpkQ6vF3U5didwb5e235Wrb+WUHaOWSDmbd7cyeWz4M0BERPrT0ToAXKTcwpiwqQVvQ5ZIpRk/MljNef0OjVMTkaGtqqqKnTt3lm0TdJGjlbuzc+fO9h0e+mrQL3g71CTTzszjh/P63kOs23GAixqPG+guiYj0WUNDA1u2bGEwza4XGSqqqqpoaGgo6RoKaiFLppy62krqaitYv0MTCkRkaIvH4+1bHolI/9Ojz5AlUmniUWNKfS3r9OhTRERESqCgFrJU2olFjSljh6miJiIiIiVRUAtZIpUmFokwdVwtOw+2saelbaC7JCIiIkOUglrIkmkPHn2ODTbzXaeqmoiIiPSRglrIkiknGokwdVwQ1LREh4iIiPSVglrIEulgMsEJo6uJR00VNREREekzBbUQpdKOO8QiEWLRCCfV1aqiJiIiIn2moBaiRGZvz1g02Gplythabc4uIiIifaagFqJkOthiJZ4JalPHDdPm7CIiItJnCmohSmU2ZI9Fgts6ZWxt++bsIiIiIsVSUAtRIh1UzrIVtSn1mSU6tmucmoiIiBRPQS1EyWxFLRrc1qn1tQCsb1ZQExERkeIpqIWofTJBJKiojaqp0ObsIiIi0mcKaiHKTibIzvoEmFo/TJuzi4iISJ8oqIUo2V5RO3Jbp9TXqqImIiIifaKgFqJEqvPyHBBU1LQ5u4iIiPSFglqIkumeK2qgzdlFRESkeApqIeptjBqgcWoiIiJSNAW1ECXbH30eua0Nmc3ZNU5NREREiqWgFqJkl+U5AG3OLiIiIn2moBaiRLrzgrdZU+tr9ehTREREiqagFqKeKmoAJ9XVskX7fYqIiEiRFNRClEh1n0wAUB2PcjiZJp2puImIiIgUQkEtRMn2Tdk739aqeBSAtkzFTURERKQQCmohat+Uvcujz8pYcJsPJVL93icREREZuhTUQpTdlL23itqhhCpqIiIiUjgFtRCleljwFqAqHtzmw0lV1ERERKRwCmohal+eI9L5tlbGVFETERGR4imohSjZ/uhTFTUREREpnYJaiLKTCaLdJhOooiYiIiLFU1ALUaLX5Tk061NERESKp6AWot6W58jO+jycVEVNRERECqegFqLsGLXujz5VURMREZHiKaiFKJF24lHDTBU1ERERKZ2CWohSae+2NAeooiYiIiJ9o6AWokQq3W2xW4DK9p0JFNRERESkcApqIUqmvNuMT+i4jpoefYqIiEjhFNRClEynu00kAKiIRjCDw6qoiYiISBEU1EKUSDnxHoKamVEZi6iiJiIiIkVRUAtRMpUm1sOjTwhmfmqMmoiIiBRj0Ac1MxtpZucMdD8KkUh7j5MJIJj5qS2kREREpBixcl3YgsXErgLeAMa4+0O9tZtZHXAusAdoc/dnzexs4HrAgX8tVz/DlEylifewPAcEFTVtyi4iIiLFKGdF7XLgTXd/BjjezBpztH8aeNjdFwNXmFkMqHT3j7j737r76jL2MzTJlCpqIiIiEp5yBrXzgc2Z15uB83K0z3T3RKYtDUwFxpjZD8zsUTM7uevFzewmM3vRzF7csWNH2T5EMZJpzzlGTRU1ERERKUY5g5plfmV5jvau/XB3/093/zvgRuCzXS/u7re7+wJ3X1BfXx9it/sumU73OOsToCoWVUVNREREilLOoLYYmJJ53QA8laN9tZlVZ9oMWN/hOm8Cg6Nklkci16PPeIRDqqiJiIhIEcoZ1B4AxpnZmcA24AYzO7dru7uvAm4F3mlmC4H73D1pZl8ysyuBdwP/VMZ+hiaZSve41ydAZSzKYVXUREREpAhlm/Xp7g4s6tD0SIfXi7ocuxO4t0vbl8vVt3JJ5lqeQxU1ERERKdKgX0dtKEmkvNeKWpUqaiIiIlIkBbUQJVNp4r1U1KriEc36FBERkaIoqIUo1/IclZr1KSIiIkVSUAtRzuU5VFETERGRIimohSj3zgRREiknlfYevy8iIiLSlYJaiIJ11HrbmSBoP5RQVU1EREQKo6AWomQ6TazXR59RAA4nNU5NRERECqOgFqJkjuU5KmOqqImIiEhxFNRClMi5PIcqaiIiIlIcBbUQ5dyZQBU1ERERKZKCWkjcgxmdve5MkKmoKaiJiIhIoRTUQpJIBctu9PboszIz61OPPkVERKRQCmohya6PlmtnAlBFTURERAqnoBaSRDqolPW+PIcqaiIiIlIcBbWQJDOPPnsLaqqoiYiISLEU1EKSTGUqanl2JjisjdlFRESkQApqIUmkc08mOLKOmipqIiIiUhgFtZC0V9Ty7kygipqIiIgURkEtJNnlOXpb8FYVNRERESmWglpIkplZn/FexqjFIkbEVFETERGRwimohSTfrE8zoyoe1axPERERKZiCWkiS7ZMJer+lVfGo1lETERGRgimoheTI8hw9V9QgmFCgipqIiIgUSkEtJNnJBNFeHn2CKmoiIiJSHAW1kOSbTACqqImIiEhxFNRCkm8yAUBlPMohVdRERESkQLGB7sBQdvefN7W/XrltHwCPrdrOqtf3dzruvWeeCEBVLMJhVdRERESkQKqohSTlmTFqpoqaiIiIhENBLSTpzPIcvewgBaiiJiIiIsVRUAtJusCKmmZ9ioiISKEU1EKSShewPIdmfYqIiEgRFNRCkh2jFslRUdM6aiIiIlIMBbWQHBmjpp0JREREJBwKaiHJLKOWc4xadlN2z1TfRERERHJRUAtJuoAxapWxCGk/soG7iIiISC4KaiFpH6OWa3mOeBRAjz9FRESkIApqIWmf9Znz0WdwuzWhQERERAqhoBaStDsRA8u1jlpMFTUREREpnIJaSNJpz7k0B0BlpqJ2KKGKmoiIiOSnoBaSVNpzLs0BRypqh5OqqImIiEh+CmohSXnu8WlwZIyaKmoiIiJSCAW1kKQLqKhlZ32qoiYiIiKFUFALScqdaO6cRmUsM+tTFTUREREpgIJaSNJpz7nYLWgdNRERESlO2YKaBa42s4Vm9q5c7WZWZ2ZXmNkFZnZ2l+v8xMyOL1c/w5LyAmZ9xrSOmoiIiBSunBW1y4E33f0Z4Hgza8zR/mngYXdfDFxhZjEAM7sMGF7GPoZGFTUREREJWzmD2vnA5szrzcB5Odpnunsi05YGpprZ24DngdaeLm5mN5nZi2b24o4dO8rR/6KkPPc+n9BxMoEqaiIiIpJfOYOaZX5leY72rv0YCbwV+F/AycCHzWx0xwPc/XZ3X+DuC+rr60PteF8UtOBtLLs8hypqIiIikl+sjNdeDEwB/go0AE/laJ9iZtXu3koQ4pa4+wsAZjYXuMPdd5exryVLZbaQyuVIUFNFTURERPIrZ0XtAWCcmZ0JbANuMLNzu7a7+yrgVuCdZrYQuM/dk2XsV1mkChijFotGiEVM66iJiIhIQcpWUXN3BxZ1aHqkw+tFXY7dCdzby3VuCL1zZZBOO9F8C6kRjFNTRU1EREQKoXXUQhIseFtIUItwSBU1ERERKYCCWkgKWZ4Dgo3ZtTOBiIiIFEJBLSSFLHgLUKmKmoiIiBRIQS0k6XT+ddRAFTUREREpnIJaSFJe2KPPqnhEsz5FRESkIApqIQkWvM1/XFUsqgVvRUREpCAKaiEpZoyatpASERGRQiiohaSQBW9BFTUREREpnIJaSNLuRAqZTKCKmoiIiBRIQS0kqXSBC96qoiYiIiIFUlALSaHLc1TFI9pCSkRERAqioBaSdMGTCaJankNEREQKoqAWgrQ7DkQLuJtVsaCiFuxZLyIiItI7BbUQpNJB6Cq0ogbQltLjTxEREclNQS0E6UxQK2wLqeCWa5yaiIiI5KOgFoKUF15Rq8pU1A5r5qeIiIjkoaAWglQfKmpaS01ERETyUVALQSanFbaOWqaiprXUREREJJ+8Qc3MImY2uj86M1S1TyZQRU1ERERCVGhF7R/M7Gwzqytrb4aoI5MJ8h+ripqIiIgUqpCgZsA/u/uzwAlm9jUzu8rMhpe5b0NGXyYTaNaniIiI5BMr4BgHvm5mlUAz8IC7P21mDWZ2jbv/rLxdHPzSRQS1I48+VVETERGR3AoJahHgSeAe77CcvrtvMbO/lqtjQ0kxsz5VURMREZFC5X306e5JYGo2pJnZJR2+93gZ+zZk9GXBW1XUREREJJ9CJxO82eH19HJ0ZChLZeqMGqMmIiIiYSo0qI0zsxlmdhZwdjk7NBQV9+gzu4WUKmoiIiKSWyFj1AB+AXwUOAx8onzdGZqykwmi+XMalbHMFlJaR01ERETyKDSobQa+S1CBmwf8tmw9GoL6suCtKmoiIiKST6FB7WFgCdAGTEZBrZP2iloBQS0SMSqiEQ5pMoGIiIjkUWhQW+Tu/wZgZmPK2J8hqb2iVsBkAoDKeITDmkwgIiIieRQa1BrN7LrM63nA35epP0PSkTFqPQe1u/+8qXODw4pte7u1v/fME8vSPxERERmaCg1qK4AdQCug/T67SGWKY4WMUQOIRY1kyvMfKCIiIse0QoPaUmCyuz9qZrvL2aGhqJgFbwFi0QiJlB59ioiISG6FrqN2OnAw81rrqHVxZFP2wo6PR4yEKmoiIiKSR6FBbR9wIPP6/DL1ZcgqZsFbCCpqybQqaiIiIpJboY8+3wQ+bmZR4Gdl7M+QlG8yQVfxqCpqIiIikl9BQc3dnwWeBTCzU8raoyGomAVvAWKRCK2JRDm7JCIiIkeBgoKamS0B3gCM4HHpxeXs1FBTzIK3oIqaiIiIFKbQR59Xu/sGADN7Txn7MyRlJ3AWOJeAeDRCUrM+RUREJI9Cg9oJZnYCQRa5GLinfF0aetLuRM2wAseoaR01ERERKUShQe0dwGogBXylfN0ZmlJpJ1Lo/Fky66hp1qeIiIjkUWhQ+x2QLQFNNLOJmdenufv3wu/W0JJyL3h8GgTrqKmiJiIiIvkUGtS+DfwISAKnAUsIHoPOKVO/hpR02gvekB2y66g5aS/uPBERETm2FBrUPunuTwCY2Ufc/d8zr/+jbD0bQlLpIitq0eA5aTLlVMQU1ERERKRnhQa1U81sKzCFYGeCHwO4+8HeTrBgZP1VBMt6jHH3h3prN7M64FxgD9Dm7s+a2RnAJQRVu6+5+5K+fMD+kJ1MUKhYJtQl02kqCt4cQkRERI41haaERcAHCPb5/GiB51wOvOnuzwDHm1ljjvZPAw+7+2LgCjOLAU3u/mXgNmBbge85INJe+GK3cKSiprXUREREJJdCg9qFwGZ3vwWYX+A55wObM683A+flaJ/p7tml+tPAVHffa2b/jSAgvq3rxc3sJjN70cxe3LFjR4FdKo9U0WPUMhU1raUmIiIiORQa1JoJJhAAzC3wHKPzGrCeo71rPxzA3f8C3EQQ1jo6HnW3AAAgAElEQVQf4H67uy9w9wX19fUFdqk8gjFqhR/fXlFLq6ImIiIivSs0XkwHxpjZJOCsAs9ZTDCmDaABeCpH+2ozq860GbC+Sx/XFfieA6LYMWrxiCpqIiIikl+hQe0+4BzgfwN/X+A5DwDjzOxMgjFmN5jZuV3b3X0VcCvwTjNbCNzn7kkz+4aZXQBcA3yp4E80AIIFb4tbngM0Rk1ERERyK3TW563ufj2AmY0p5AR3d4JJCFmPdHi9qMuxO4F7u7R9tsC+DbhUsRU1jVETERGRAuStqJnZeGCZmV1hZucBHy5/t4aWdLEVtYgqaiIiIpJfzqBmZtcAcYKZmfXAZKCuH/o1pBS74G37rE/t9ykiIiI55Hv0WQu0AE8Df3D3v5pZRfm7NbSknSIffaqiJiIiIvnle/T5urs3A0+5+18B3L2t7L0aYoI9Ows/XhU1ERERKUS+oJatuLWXfjKzNaWDYmd9xjVGTURERAqQ79Hnz81sP8EWnU6wxlkNcFzZezaE9HmMmmZ9ioiISA75gtpCd1/TscHMZpqZZZbfEPq2KbuhipqIiIjkljOodQ1pmbam8nVnaCr20aeZEYuaKmoiIiKSUxE7VEpvUkXO+oRgLTXt9SkiIiK5KKiFoNgFbyHYnUAVNREREclFQS0EwRZSxZ0Ti0ZIKKiJiIhIDgpqIehLRS0WMZJ69CkiIiI5KKiFoNhZnxDsTqCKmoiIiOSioFYidyftFF9RixpJLc8hIiIiOSiolSiVWU6umAVvQRU1ERERyU9BrUTZ7TqLX55DY9REREQkNwW1EqUyYauQR5+pZIKX/vQwe3a8kamoKaiJiIhI7/JtISV5tD/6zJPT9u7czn/+4OtsXbeK2hGjGXfpzSSp64ceioiIyFClilqJ0gVU1DatWc4dX/w7dmzdyNvf+7dEYzE2/uafaXuj2w5dIiIiIu0U1Ep0pKLWc1Bzdx696wdUVtfwoVu+x5lvv5IP/ONtVAwfQ/qpO2g9uL8/uysiIiJDiIJaifJV1Da/toI3N61j4Tuvpe74BgBGjKln6mUfwVJtvPrs4/3WVxERERlaFNRKlK+i9pfHHqSqZhizz7qwU/vICZNJj2rgpcUP465JBSIiItKdglqJsstz9FRR2797J6tfepq5576disqqTt+LRyIkJ51F89aNbFm7sj+6KiIiIkOMglqJclXUXlr8MOl0mtMvele378WiRmriXCqqqlmy+JGy91NERESGHgW1EmXHqEW73MlUMsGSPz3C9Dn/jTHjJnQ7rzoehVgl0xacz8oXntSkAhEREelGQa1EvS14u7HpVQ7u2828Cy7t8byaimAJu6lnvI1kQpMKREREpDsFtRL19uhzw4qlRKIxJjfO7fG82sooAJX1J1I3/gTWvvJCeTsqIiIiQ46CWomOPPrsHNTWr1hCw7RGKqqqezwvW1FraUsy+eS5bFqznLa2tvJ2VkRERIYUBbUSpTMVtUiHilrL/r28sWkdU06Z3+t5tRVBRe3g4RSTT55H4vAhXn311fJ2VkRERIYUBbUSpXpYnmPDqpfBncknz+v1vIpYhGjEaGlLctKsOWDG888/X+7uioiIyBCioFainsaobVixlMrqWiZMntHreWZGbUWUg20pqmuHM/6kaQpqIiIi0omCWomObCEVfO3ubFixhEmNpxGJRnOeW1MRo6UtBcCkk+eybNkyWlpaytpfERERGToU1ErUtaK2e8fr7Gl+M+f4tKyayigth5MATG6cSyKRYOnSpeXrrIiIiAwpCmol6jrrc8OKIGhNPqX38WlZtRUxDmYqaifMmE08Hue5554rU09FRERkqFFQK1G2ohbpENRGjKlnzHET855bUxGlpS2oqFVUVjFnzhyNUxMREZF2Cmolyu5MkH30uXX9Kk6YcQrWw96fXdVUxGhtS7Uv8XHWWWexcuVK9u3bV74Oi4iIyJChoFaiTE4jYsb+PTvZt6uZiZNnFnRubWUUB1ozjz/nzp2Lu7N8+fIy9VZERESGEgW1EnUco7ZtwxoAJkwpLKhldyc4mHn8OXv2bABWrFgRdjdFRERkCFJQK9GRMWqwbf0aLBLh+BOnFnRudneClsNBRW3kyJGceOKJ2qFAREREAAW1knUco7ZtQxPjGiYTr6wq6NyayiP7fWbNnj1bjz5FREQEUFArWTrtZHeP2rahKeduBF217/eZGaMGcOqpp/L666/T3Nwcaj9FRERk6FFQK1HKnYgZu7dv49DBA0wscHwaHBmjll30Fo6MU9PjTxEREVFQK1E67UQjxtb1TUDhEwkg2Jg9HrX2baQAGhsbiUQimlAgIiIiCmqlylbUXt+whlhFJfUTTirq/JoOuxMA1NbWMmXKFFXUREREREGtVKl0sCvB1vVNjJ80Le9G7F3VdtidIOvUU09l+fLleGZGqYiIiBybyhbULHC1mS00s3flajezOjO7wswuMLOzM23/3cy+ZGaLzGxWufpZqrQ7EU/xxqZ1TChwoduOaipjHDzcOajNnj2bXbt2sW3btrC6KSIiIkNQOStqlwNvuvszwPFm1pij/dPAw+6+GLjCzGLA6+7+ZeAbwI1dL25mN5nZi2b24o4dO8r4MXJLp53I/jdIth3uW1CriHYaowZBRQ3QMh0iIiLHuHIGtfOBzZnXm4HzcrTPdPdEpi0NTHX3v2S+PgX4fdeLu/vt7r7A3RfU19eXo/8FSbnDnqDyNX7StKLPD8aoda6ozZw5k3g8rnFqIiIix7hyBjXL/MryHO1d++EAZjYJOOjuj5Wni6VLpR3fs5V4RSWjx40v+vzaiiiHEun2hXMBKioqmD59OqtWrQqzqyIiIjLElDOoLQamZF43AE/laF9tZtWZNgPWm9lkYIK7P2Bmo8vYz5Kk047v2UZ9wyQikeImEkDPuxMAzJo1i9WrV2tCgYiIyDGsnEHtAWCcmZ0JbANuMLNzu7a7+yrgVuCdZrYQuA8YCfwYuMrM/gX4pzL2syTJdJrU7q0cd8KU/Af3oH2/zy7j1GbNmsWuXbsYyPF3IiIiMrBi5bqwB6WgRR2aHunwelGXY3cC93a5xNvL1LVQJQ/sxQ8f5LgTJvfp/OzuBD2NUwNoampi3LhxpXVSREREhiSto1aiw7u2ADCurxW1ykxF7XD3ihrA6tWrS+idiIiIDGUKaiVqaw6C2nENpVXUuj76HDFiBBMmTFBQExEROYYpqJUosXsL0WFjqKod1qfza9rHqCW7fa+xsVFBTURE5BimoFai5K6tVNQ19Pn8eDRCRSzSbXcCCMap/fWvf6W1tbWULoqIiMgQpaBWgmSijfTeN0sKapDd7zPVrX3WrFmk02lee+21kq4vIiIiQ5OCWgmat20GT1M1trSg1tPuBKAJBSIiIsc6BbUSvLl5PQA19SeWdJ2e9vsEmDhxIsOGDVNQExEROUYpqJVg++b1EI1TOeq4kq5TWxnrcYxaJBJh5syZNDU1lXR9ERERGZoU1Erw5ub1MHI8sVhpt7G3ihoEEwpWr15NOp0u6T1ERERk6FFQ6yN3583NG2DkeCJm+U/IoaYixuFkmsPJnicUtLS0sGXLlpLeQ0RERIYeBbU+am5upmX/XnzEeCKR0oJadneCPS2Jbt/ThAIREZFjl4JaH2XHjaVGTCAaQkUNYHdLW7fvTZ8+nUgkoqAmIiJyDFJQ66NsUEsPH0+01IpaZneCXQe7B7WqqiomT56soCYiInIMUlDro6amJoaPHksqXl36GLXKTEXtYPdHnxA8/lRQExEROfYoqPVRU1MTx50wBYBoiXexvaLWw6NPCGZ+vv766+zZs6e0NxIREZEhRUGtD9ra2li/fj31DZMBwhuj1sOjTwg2ZwdYs2ZNSe8jIiIiQ4uCWh+sX7+eZDLJ2IZJACXP+oxGjKp4pMcxahBU1EAzP0VERI41Cmp9kJ1IUDdhEkDJY9QgqKr1NOsToL6+nrFjxyqoiYiIHGMU1PqgqamJiooKRtRPACh51icEuxP0VlGDIzsUiIiIyLFDQa0PmpqamD59OkSCSQCljlEDqM1RUYNg5ufatWtJJHqeGSoiIiJHHwW1PmhqamLmzJmk0g6UPkYNgopab8tzQBDUEokEGzZsKPm9REREZGhQUCtSc3MzO3fuZObMmaQzQa3U5TkAaitjOR99ZreSWrVqVelvJiIiIkOCglqRshMJZs6cScozFbVQJhNEaU2kOJTovjE7wKRJk6ioqGh/fxERETn6KagVKRuUZsyY0f7oM4zJBLU59vsEiMVizJgxQxU1ERGRY4iCWpGampo47rjjGD16NOlMRS2MyQQ1lb3v95k1c+ZMmpqa8Mz7ioiIyNFNQa1ITU1NzJgxAyDkyQS59/uEYIeC3bt3s3379pLfT0RERAY/BbUiZLeOyg7sD3OMWr79PkE7FIiIiBxrFNSKsGHDBhKJRHtgSqeD9lAWvK3Mvd8nKKiJiIgcaxTUitBxIgHQYYxa6deujkcxyz1Gbfjw4TQ0NCioiYiIHCMU1IrQ1NREPB5n8uTJQLhj1KIRY2R1POfuBKCtpERERI4lCmpFaGpqYtq0acRiwWPKMJfnABhdU5GzogbBhIKNGzfS0tISynuKiIjI4KWgVoTs1lFZ6RAnEwCMrimsouburFmzJpT3FBERkcFLQa1AO3fupLm5uVNQC7uiNqa2gl05lueAoKIGaIcCERGRY4CCWoE6bh2VFeaCtxA8+tyTp6I2YcIEhg8frnFqIiIixwAFtQJlHzV2rqgFv4cxmQCyFbW2nDsPmJkmFIiIiBwjFNQKtHr1aurr6xkzZkx725ExauG8x+jaCg4n07T2sjF71qxZs1izZg3p7EJuIiIiclRSUCvQmjVrOlXToAxj1GoqgNxrqUEQ1FpaWti0aVMo7ysiIiKDk4JaAdydeDzO7NmzO7WHPkatNghqzQfyBzXQhAIREZGjXWygOzAUmBm/+tWvurWHueAtwOSxtQCs3X6AuSeM6vW4adOmEY1GWbVqFRdffHEo7y0iIiKDjypqJchuyh7Wo8/JY2upikdYuW1fzuMqKyuZPHmyKmoiIiJHOQW1EqTTjhHegrfRiDHz+BGsej13UINgPTXN/BQRETm6KaiVIJUO77Fn1snjh7Py9X05l+iAYJmQN954g927d4f6/iIiIjJ4KKiVIO0e2tIcWSePH8He1gSv7z2U8zhNKBARETn6KaiVIJX20ManZZ08YQRA3nFq2aCmx58iIiJHr7IFNQtcbWYLzexdudrNrM7MrjCzC8zs7ExbhZl9y8yuLVcfSxVU1MINajOPD4JavnFqdXV11NfXK6iJiIgcxcpZUbsceNPdnwGON7PGHO2fBh5298XAFWYWA6YBq8rYv5KVo6I2rDLGpLoaVhYwoWDWrFl69CkiInIUK2dQOx/YnHm9GTgvR/tMd09k2tLAVHdfCfS6l5KZ3WRmL5rZizt27Ai984VIu4e22G1HjeMLm/k5a9Ys1q1bR1tb7gVyRUREZGgqZ1CzzK8sz9HetR+5pzwC7n67uy9w9wX19fUldbSvUmkPfdYnBBMK/rqzhQOHkzmPmzlzJolEgvXr14feBxERERl45Qxqi4EpmdcNwFM52lebWXWmzYAhkTxSHt72UR01jg/Gqa3OU1VrbAyeJmucmoiIyNGpnEHtAWCcmZ0JbANuMLNzu7a7+yrgVuCdZrYQuM/dk2ZWA8wGZptZRRn72WfpMoxRgyMzP/M9/jzppJOoqqpSUBMRETlKlW2vTw9WbF3UoemRDq8XdTl2J3Bvl7YWgkkGg1YqHf46agDjR1Yxsjqed0JBNBpl+vTpCmoiIiJHKa2jVoK0l2eMmplx8vgRrHx9f95jTz75ZJYvX04q1eu8CxERERmiFNRKkCrTrE8IHn82vbGPVDr3vIr58+dz8OBB1q5dW5Z+iIiIyMBRUCtBukyzPiGYUHAokWZD88Gcx82bNw+AJUuWlKUfIiIiMnAU1EpQjgVvs07OzPzMN06toaGBsWPHsnTp0rL0Q0RERAaOgloJDralqKmIluXa08YNIx61vDM/zYz58+crqImIiByFFNT6KJV29rYkGFVdnpVDKmIRpo0bnndzdggef27ZsoXt27eXpS8iIiIyMMq2PMfRbvv+Q6TcGV0bD+2ad/95U6evq2IRlmza3a39vWee2Onr7Di1pUuXcvHFF4fWHxERERlYqqj10dbdrQBlq6gBjB9Vzf5DybxbSTU2NlJZWanHnyIiIkcZBbU+2pINajXhVdS6Gj+yCoDX97bmPK6iooLZs2crqImIiBxlFNT6aOueIDyNriljRS0b1PYcynvs/PnzWblyJa2tuUOdiIiIDB0Kan20ZXcrNRVRKmLlu4U1FTFGVsd5Y1/+oDZv3jySySTLly8vW39ERESkfymo9dGW3S1lraZljR9ZxbY9+atk8+bNw8x44YUXyt4nERER6R8Kan20dU9rWcenZY0fWUXzgcMkUumcx40aNYrZs2fzzDPPlL1PIiIi0j8U1PrA3dm6u7VfKmrHj6wm7bB93+G8x5599tm88sor7NuXf+01ERERGfwU1Pqg+UAbh5PpfqmonTC6GoDVb+QPX+eccw6pVIrnn3++3N0SERGRfqCg1gf9MeMza1RNBTOPG87zG3blffx52mmnUVtby7PPPlv2fomIiEj5Kaj1wdZ+WEOto3Omj+Xg4STLNu/JeVw8Hudv/uZvePrpp3H3fumbiIiIlI+CWh9s2d0ClHdXgo6mjK1l/Mgqnl7bnDeALVy4kK1bt7Jx48Z+6ZuIiIiUj4JaH2zd08rwqhjVFdF+eT8zY+G0sWzff5i12w/kPHbhwoUAmv0pIiJyFFBQ64Mtu1tpGF3Tr+85p2Ekw6tiPL22OedxJ554IieeeKKCmoiIyFFAQa0Ptu5uZeKo6n59z1gkwllT6nht+wGa3tif89iFCxfy/PPPazspERGRIU5BrUjuztY9rTSM7t+gBnDGpDHEo8ZPn16f87h3vOMdtLa2snjx4v7pmIiIiJSFglqR9rYmOHA4OSBBraYyxvwTR3P/0m3s2N/7Arinn3469fX1PPLII/3YOxEREQmbglqRtmSW5ujvR59ZC6eOpS2V5hfP9z6rMxqNcskll/Dkk0+yf3/ux6QiIiIyeCmoFSm72O3EAaioAYwdXslbG8dx1/MbOZRI9XrcpZdeSltbG3/84x/7sXciIiISJgW1ImUrav0967OjD54zhV0H2/jVXzb3esycOXNoaGjg4Ycf7seeiYiISJgU1Iq0dXcr1fEoo/tpV4Ke/M2UMZw1pY5/+X1Tr2PVzIxLL72U5557jl27dvVzD0VERCQMCmpF2rK7hYbR1ZjZgPXBzPjqlbM5lEjztYdX9nrcZZddRiqV4re//W0/9k5ERETCoqBWpK17WgdsfFpHU+uH8bfnT+H+l7fxTC+L4E6fPp1TTz2VX/ziF6TTuTd0FxERkcFHQa1IW/f0/2K3vfm7t0zjpLoa/vH+5T1OLDAzPvCBD7Bx40b+9Kc/DUAPRUREpBQKakU4cDjJnpbEgE4k6KgqHuUrl89mQ/NBfvxEz4vgvu1tb2PixInceeed/dw7ERERKVVsoDswlGzdPbBLc2Td/edNnb6e0zCS7z3+GmYwdlhle/t7zzyRWCzG9ddfz9e//nVefvll5s6d29/dFRERkT5SRa0IW/e0AAzIrgS5XHrqeKIR48GXt+Hu3b5/1VVXMWLECH7+85/3f+dERESkzxTUitC+htogGaOWNaIqzttPOZ61Ow7w7Lqd3cJabW0t1157Lb///e9Zvnz5APVSREREiqWgVoStu1upiEY6PV4cLM6cPIbp44bx8Kuv8/Nn/8rulrZO3//Qhz5EXV0dt9xyC6lU7zsaiIiIyOChoFaELZmlOSKRgVtDrTcRM64/exLvmjOejbta+M5jr/HzZzaQSgfVteHDh/PZz36WFStWsGjRogHurYiIiBRCQa0IW3YPnqU5ehIx46ypY/n4RdOZNLaGWx5ayTU/epa124ON2S+55BLOPvtsbrvtNrZv3z7AvRUREZF8FNSKsHWQB7Ws0TUVXH/WJG5792lsaD7Ipd99mp88tR53+OIXv0gikeAf/uEfaGtry38xERERGTAKagU6lEjRfODwoJvx2Rsz48p5Dfz+78/n/Bn1fPXhVVx7x/NEhtdzyy238Pzzz/OlL32px1miIiIiMjhoHbUCbd0zONZQK0Z2vbULZtQzoirOf72yjYu+/QSXnTqZ8y5/P/fffxe7fBg//ubnB7inIiIi0hNV1AqUXex2sOxKUAwz4/STRvOxi6bTMKqa+5Zu5ZURZzKqcSFPPnAX/+fTn+eN3QcGupsiIiLShSpqBdoySHYlKMXomgpuPGcyf96wi1e37OXNxitJtEV5/OHf8NizLzHsghuZN3MKpzWM5LQTRnFawyhG1sQHutsiIiLHLAW1Am3d00I0Yhw3fPCtoVaMiBlnTanjrCl1uDv73z6Lvzy9gD//+ge0PvhVXpi+kMdOOg+qRgAwuibOyOoKRlTHOGPSGI4bUcW4EZWMGx78ftyIKoZV6sdIRESkHPR/2AJt3d3K+JFVxKJHz9NiM2NEVZyL3noxp592Gk89eDfLnvkDw9Y+w/iZ86idMp9E9QwOEnz+X765idZE98VyayqijBteyZjaCsbUVjCqJvt7nKpYlFjUiEUixCJGLGrUD69kUl0tE0ZVEx2Ea9KJiIgMFmULamZmwFXAG8AYd3+ot3YzqwPOBfYAbe7+rJmdBowFaoBl7r6pp/fpL4N9DbVSjao/nnd98JMsfOe7eeEPD7D6xafZsuIFAEaMqafhpKmMqh9P7aixRKpHkIxU0EacwxbnkMdoSbaxa6ez9Y00rUmnpS1N0gEMzMAiR15nVMQinDCmhsljaxlWEeNwOk0imaYtmeZwMkU8GqF+WFC5qx9WSf2ISmoro+w/lGJfayL4dSjB4USakdVxRtfEGVVbweiaODUVUbbvb+P1Pa1s2d3K63tb2XHgMGNqKpgwqpqJo6qZMLqaCaOqqIhGSKadVNpJu5NMOU732bBRMyrjUSpjESqiESpjEdyhNZGiJZGi5XCSlrYUyTRUV0SoqYhSHY9SE49SEYuQTDltqXT7r1TaqYxFqIpFqYpFqYxHqIxHMQN36NaFzK0za3+J08uxXc4zC6qpljnfHRKp4F5nf0+7E49GqIhFOv3erQPZr7J/tBhmtN+/VNpJpyGVmVGcfU/DuvUlYtbxRyL4TN75PehyXCrtJFJpkiknkU6TTkM0YsQiRjQa/B4xwx2S6eA+Z39FssdFjHg0QjRidP2nQvbts/3o+rOQPSPbn57+qWFdPpS7k/Ij9yWVdtwds6AvUTMikeBnrGs/+qpjD3rqT/vPDp0/Y/bPM3uNjvej63HBtbN/xn3/R1e2P9n36fQ5utznnj5Lt+t1uVZPf4Zh9LsUHfud78+6tx72dC/SmT8n9yOfv+t/R93+/uh23d7v91CVvd+F/pxVVgyeYT/lrKhdDrzp7s+Y2YfNrNHdV/XUDlwHfNHdE2b2z2b2AnCdu38KwMy+DXyyjH3Na+ueVs6aWjeQXegXY46byDve/3dc/N6/Zev6Jja/tpzXN67lzU3r2bBiKYm2wwVdJ575lc+2zC8REZHBIDKsjhV/eXqgu9GunEHtfOBfM683A+cBq3ppn+nuiUxbGjgFGN3hWpO7XtzMbgJuynx5wMyaQu19D54Hbnt3+5djgeZyv6cAutf9Sfe6/+he9x/d6/5x1NznfqgknlTogeUMakbniq3naO9p4FfOu+TutwO3l9LBUpjZi+6+YKDe/1iie91/dK/7j+51/9G97h+6z+VRzpHxi4EpmdcNwFM52lebWXYAmAErgP0drrW+jP0UERERGZTKGdQeAMaZ2ZkEw5BuMLNzu7Znxq3dCrzTzBYC97l7ErjTzN5mZu8AvlPGfoqIiIgMSmV79OnBFItFHZoe6fB6UZdjdwL3dmlbWq6+hWTAHrseg3Sv+4/udf/Rve4/utf9Q/e5DEybcouIiIgMTkfP6q0iIiIiRxkFNRERESmYBS4Z6H4cK7SFVJF623FBSmNm84Gvu/s7Ct2pwszeDhwEJgAPuHvbQPV/KDCzkcDNwBigBfgZMBNIAq+7+3IzO4fgH3D1wB/dfY+ZvQvYBYx39/8YmN4PLWYWAT4OnECw9vMt6Ge6bMxsNvB+golpus9lZGbfBEYBazPrl+rvkDJTRa147TsrAMdndlaQEmSWZtnXoenTwMPuvhi4wsxiBDtV/DETjD9hZscBp2X+HJYBN/Z3v4eg6cDX3P2TBItK3+zuv3X3PwAfNLNK4BJ3fxJ4FPiUmc0FRmbu877MLGzJrwL4AfAZ4PfoZ7pszKyB4Od5FLrP/eF37v637v4t4P/o75DyU1Ar3vkEOyrAkZ0VpATu3uruazs0FbJTxd8A2zNfbyL4c5Ec3P1Fd3czixKsHt5xZezjgFlAW+bYg8BsOv+86z4XyN0PAVXA9cAHgFn6mQ6fmY0iuJd/zjTp747ye6+Z/TpTWeu4a5D+DikTBbXi9bbjgoSnkJ0qOv455NuWXDp7D0Glp9O+3UA1ue8z6D4XzN33uvsdBIt8X9TDIfqZLt3/BBoJqmKnApf1cIzuc4jc/cPu/j+BYejvkH6hoFa8xfS844KEp5CdKp4jGF8CcCLwZP91b+jKjBV5ILN24ZYO39oGLAVqM8fVAMvp/PN+ArrPfREH/qCf6fC5++3u/q8E4y1fBW7Tfe436wnGAmbp75Ay0TpqRcpMJng3sAGoc/dH8pwiBciM9ftP4J3AXuBCgv/wU+7+vJnNIxgQHAVWZgYEXwrsBo4nGJeiAcE5mNn/I3g0sQsYB/wUGEkwPrA5MxA4+yh/FPBkZiDw1cBW4Hh3v38Aurs72IYAAAH1SURBVD7kmNlFBPf6ZYJHQk+gn+myMbNJBFXiz6P7XDZmdgbwVuAFgskZrxJUMvV3SBkpqImIiIgMUnr0KSIiIjJIKaiJiIiIDFIKaiIiIiKDlIKaiIiIyCCloCYiIiIySCmoiYiIiAxSCmoiIiIig5SCmohID8zsy5l9UUVEBowWvP3/27tjnSqiKArD/4rE3khHQ2HUwooosYTayoaa0tpnkMoQGh6AEhsfwIbqmltCQRRJoCeUSmICbIsZyQ3R9s4k5/+amTnVqiYrc3JmS1IvydOq+tHfp3xBShrYwtABJGkISTaAV8AJ3eiyr8B6km/AAfAOeJNkhW4sUYCfwGfgLXAKLFbVpwHiS2qERU1Sqy6A46raS3IAfKDbZXgPd0UOuhmSm1V11a9vARPgF/Bs/rEltcSiJklwSTes/vfM2k1/vQYeAVf980Pge1WdAdO5JZTUJIuapJatJjkBXgD7wMckL4FzYCnJE2AL2E4yAY6AXWAnyRQ4rKovA2WX1AAPE0hqUpI1YLmq9u6t/z3peQs8qKrrOUeTpDt+UZOkGVV1M/NoSZM0KP+jJqlVz4HXSR4PHUSS/setT0lNSrJAd2AgVXU7dB5J+heLmiRJ0ki59SlJkjRSFjVJkqSRsqhJkiSNlEVNkiRppCxqkiRJI/UHEQr8e18puNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plt_distribution(data, obj_col):\n",
    "    plt.figure(figsize=(10,6))\n",
    "    sns.distplot(data[obj_col] , fit=norm);\n",
    "\n",
    "    # 获取数据分布曲线的拟合均值和标准差\n",
    "    (mu, sigma) = norm.fit(data[obj_col])\n",
    "    print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "    # 绘制分布曲线\n",
    "    \n",
    "    plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "                loc='best')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title('SalePrice distribution')\n",
    "    \n",
    "    # 绘制图像查看数据的分布状态\n",
    "    fig = plt.figure()\n",
    "    plt.figure(figsize=(10,6))\n",
    "    tmp = stats.probplot(data[obj_col], plot=plt)\n",
    "    plt.show()\n",
    "plt_distribution(df, 'price')        # 目标变量变换前的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 4.82 and sigma = 0.64\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标变量呈现明显的偏态分布，需要将它变换成无偏的正态分布，因为通常的线性模型所针对的数据都是正态分布的数据。\n",
    "df[\"price\"] = np.log1p(df[\"price\"]) # 对数变换\n",
    "plt_distribution(df, 'price') # 变换后的分布情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.顺序特征编码-处理楼层信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['中层(共10层)', '中层(共11层)', '中层(共12层)', '中层(共13层)', '中层(共14层)',\n",
       "       '中层(共15层)', '中层(共16层)', '中层(共17层)', '中层(共18层)', '中层(共19层)',\n",
       "       '中层(共20层)', '中层(共21层)', '中层(共22层)', '中层(共23层)', '中层(共24层)',\n",
       "       '中层(共25层)', '中层(共26层)', '中层(共27层)', '中层(共28层)', '中层(共29层)',\n",
       "       '中层(共30层)', '中层(共31层)', '中层(共32层)', '中层(共33层)', '中层(共34层)',\n",
       "       '中层(共35层)', '中层(共38层)', '中层(共6层)', '中层(共7层)', '中层(共8层)', '中层(共9层)',\n",
       "       '中楼层 (共10层)', '中楼层 (共11层)', '中楼层 (共12层)', '中楼层 (共13层)',\n",
       "       '中楼层 (共14层)', '中楼层 (共15层)', '中楼层 (共16层)', '中楼层 (共17层)',\n",
       "       '中楼层 (共18层)', '中楼层 (共19层)', '中楼层 (共20层)', '中楼层 (共21层)',\n",
       "       '中楼层 (共22层)', '中楼层 (共23层)', '中楼层 (共24层)', '中楼层 (共25层)',\n",
       "       '中楼层 (共26层)', '中楼层 (共27层)', '中楼层 (共28层)', '中楼层 (共29层)',\n",
       "       '中楼层 (共30层)', '中楼层 (共31层)', '中楼层 (共32层)', '中楼层 (共33层)',\n",
       "       '中楼层 (共34层)', '中楼层 (共35层)', '中楼层 (共36层)', '中楼层 (共37层)',\n",
       "       '中楼层 (共38层)', '中楼层 (共39层)', '中楼层 (共40层)', '中楼层 (共41层)',\n",
       "       '中楼层 (共42层)', '中楼层 (共43层)', '中楼层 (共44层)', '中楼层 (共45层)',\n",
       "       '中楼层 (共46层)', '中楼层 (共47层)', '中楼层 (共48层)', '中楼层 (共49层)',\n",
       "       '中楼层 (共4层)', '中楼层 (共50层)', '中楼层 (共51层)', '中楼层 (共53层)',\n",
       "       '中楼层 (共54层)', '中楼层 (共55层)', '中楼层 (共56层)', '中楼层 (共5层)', '中楼层 (共6层)',\n",
       "       '中楼层 (共7层)', '中楼层 (共8层)', '中楼层 (共9层)', '低层(共10层)', '低层(共11层)',\n",
       "       '低层(共12层)', '低层(共15层)', '低层(共16层)', '低层(共17层)', '低层(共18层)',\n",
       "       '低层(共19层)', '低层(共20层)', '低层(共21层)', '低层(共22层)', '低层(共23层)',\n",
       "       '低层(共24层)', '低层(共25层)', '低层(共26层)', '低层(共27层)', '低层(共28层)',\n",
       "       '低层(共29层)', '低层(共30层)', '低层(共31层)', '低层(共32层)', '低层(共33层)',\n",
       "       '低层(共34层)', '低层(共39层)', '低层(共6层)', '低层(共7层)', '低层(共8层)', '低层(共9层)',\n",
       "       '低楼层 (共10层)', '低楼层 (共11层)', '低楼层 (共12层)', '低楼层 (共13层)',\n",
       "       '低楼层 (共14层)', '低楼层 (共15层)', '低楼层 (共16层)', '低楼层 (共17层)',\n",
       "       '低楼层 (共18层)', '低楼层 (共19层)', '低楼层 (共1层)', '低楼层 (共20层)',\n",
       "       '低楼层 (共21层)', '低楼层 (共22层)', '低楼层 (共23层)', '低楼层 (共24层)',\n",
       "       '低楼层 (共25层)', '低楼层 (共26层)', '低楼层 (共27层)', '低楼层 (共28层)',\n",
       "       '低楼层 (共29层)', '低楼层 (共2层)', '低楼层 (共30层)', '低楼层 (共31层)',\n",
       "       '低楼层 (共32层)', '低楼层 (共33层)', '低楼层 (共34层)', '低楼层 (共35层)',\n",
       "       '低楼层 (共36层)', '低楼层 (共37层)', '低楼层 (共38层)', '低楼层 (共39层)',\n",
       "       '低楼层 (共3层)', '低楼层 (共40层)', '低楼层 (共41层)', '低楼层 (共42层)',\n",
       "       '低楼层 (共43层)', '低楼层 (共44层)', '低楼层 (共45层)', '低楼层 (共46层)',\n",
       "       '低楼层 (共47层)', '低楼层 (共48层)', '低楼层 (共49层)', '低楼层 (共4层)',\n",
       "       '低楼层 (共50层)', '低楼层 (共51层)', '低楼层 (共53层)', '低楼层 (共54层)',\n",
       "       '低楼层 (共55层)', '低楼层 (共56层)', '低楼层 (共5层)', '低楼层 (共6层)', '低楼层 (共7层)',\n",
       "       '低楼层 (共8层)', '低楼层 (共9层)', '共1层', '共2层', '共3层', '共4层', '共5层',\n",
       "       '地下(共3层)', '地下室 (共18层)', '地下室 (共1层)', '地下室 (共2层)', '地下室 (共32层)',\n",
       "       '地下室 (共35层)', '地下室 (共3层)', '地下室 (共4层)', '地下室 (共5层)', '地下室 (共6层)',\n",
       "       '暂无数据', '高层(共10层)', '高层(共11层)', '高层(共12层)', '高层(共13层)', '高层(共14层)',\n",
       "       '高层(共15层)', '高层(共16层)', '高层(共17层)', '高层(共18层)', '高层(共19层)',\n",
       "       '高层(共20层)', '高层(共21层)', '高层(共22层)', '高层(共23层)', '高层(共24层)',\n",
       "       '高层(共25层)', '高层(共26层)', '高层(共27层)', '高层(共28层)', '高层(共29层)',\n",
       "       '高层(共30层)', '高层(共31层)', '高层(共32层)', '高层(共33层)', '高层(共6层)',\n",
       "       '高层(共7层)', '高层(共9层)', '高楼层 (共10层)', '高楼层 (共11层)', '高楼层 (共12层)',\n",
       "       '高楼层 (共13层)', '高楼层 (共14层)', '高楼层 (共15层)', '高楼层 (共16层)',\n",
       "       '高楼层 (共17层)', '高楼层 (共18层)', '高楼层 (共19层)', '高楼层 (共20层)',\n",
       "       '高楼层 (共21层)', '高楼层 (共22层)', '高楼层 (共23层)', '高楼层 (共24层)',\n",
       "       '高楼层 (共25层)', '高楼层 (共26层)', '高楼层 (共27层)', '高楼层 (共28层)',\n",
       "       '高楼层 (共29层)', '高楼层 (共30层)', '高楼层 (共31层)', '高楼层 (共32层)',\n",
       "       '高楼层 (共33层)', '高楼层 (共34层)', '高楼层 (共35层)', '高楼层 (共36层)',\n",
       "       '高楼层 (共37层)', '高楼层 (共38层)', '高楼层 (共39层)', '高楼层 (共40层)',\n",
       "       '高楼层 (共41层)', '高楼层 (共42层)', '高楼层 (共43层)', '高楼层 (共44层)',\n",
       "       '高楼层 (共45层)', '高楼层 (共46层)', '高楼层 (共47层)', '高楼层 (共48层)',\n",
       "       '高楼层 (共49层)', '高楼层 (共4层)', '高楼层 (共50层)', '高楼层 (共51层)',\n",
       "       '高楼层 (共53层)', '高楼层 (共54层)', '高楼层 (共55层)', '高楼层 (共56层)',\n",
       "       '高楼层 (共5层)', '高楼层 (共6层)', '高楼层 (共7层)', '高楼层 (共8层)', '高楼层 (共9层)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['floor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 定义函数对顺序变量进行编码\n",
    "# 10层为标准\n",
    "import re\n",
    "level = 0\n",
    "def process_floor(x):\n",
    "    floor_level = x[0:1]\n",
    "    floor_level_num = int(re.findall(r\"\\d+\\.?\\d*\",x)[0]) if re.findall(r\"\\d+\\.?\\d*\",x) else 1\n",
    "    if floor_level == '低' and floor_level_num < 10:\n",
    "        level = 0\n",
    "    elif floor_level == '中' and floor_level_num < 10:\n",
    "        level = 1\n",
    "    elif floor_level == '高' and floor_level_num < 10:\n",
    "        level = 2\n",
    "    if floor_level == '低' and floor_level_num >= 10:\n",
    "        level = 3\n",
    "    elif floor_level == '中' and floor_level_num >= 10:\n",
    "        level = 4\n",
    "    elif floor_level == '高' and floor_level_num >= 10:\n",
    "        level = 5\n",
    "    else:\n",
    "        level = 0\n",
    "    return level\n",
    "## 顺序变量特征编码\n",
    "cols = ['floor']\n",
    "for col in cols:\n",
    "    df[col] = df[col].apply(process_floor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.类别类编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['region','type', 'construction_area', 'orientation', 'decoration','elevator','purposes','house_structure']\n",
    "for col in cols:\n",
    "    df[col] = df[col].astype(str)\n",
    "del cols, col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.字符型特征标签编码(独热编码(OneHotEncoder)和标签编码(LabelEncoder)编码)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['construction_area']=df['construction_area'].astype(float)\n",
    "## 年份特征的标签编码\n",
    "# str_cols = [\"year\"]\n",
    "# for col in str_cols:\n",
    "#     df[col] = LabelEncoder().fit_transform(df[col])\n",
    "\n",
    "## 为了后续构建有意义的其他特征而进行标签编码\n",
    "lab_cols = ['orientation','elevator', 'purposes', 'house_structure','decoration']\n",
    "\n",
    "for col in lab_cols:\n",
    "    new_col = \"lab_\" + col\n",
    "    df[new_col] = LabelEncoder().fit_transform(df[col]) \n",
    "        \n",
    "del col,lab_cols,new_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3室2厅2卫     19729\n",
       "2室1厅1卫     16912\n",
       "2室2厅1卫     12515\n",
       "1室1厅1卫      9721\n",
       "3室2厅1卫      8839\n",
       "           ...  \n",
       "8室2厅2卫         1\n",
       "9室2厅6卫         1\n",
       "11室2厅5卫        1\n",
       "4室5厅3卫         1\n",
       "0室1厅0卫         1\n",
       "Name: type, Length: 206, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 单独处理户型\n",
    "# 用 str.extract() 方法，将\"室\",\"厅\",\"卫\"都提取出来，单独作为三个新特征\n",
    "df['type_room_num'] = df['type'].str.extract('(^\\d).*', expand=False)\n",
    "df['type_hall_num'] = df['type'].str.extract('^\\d.*?(\\d).*', expand=False)\n",
    "df['type_wash_num'] = df['type'].str.extract('^\\d.*?\\d.*?(\\d).*', expand=False)\n",
    "df['type_room_num'] = df['type_room_num'].fillna('1').astype('int64')\n",
    "df['type_hall_num'] = df['type_hall_num'].fillna('1').astype('int64')\n",
    "df['type_wash_num'] = df['type_wash_num'].fillna('1').astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
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       "      <th>行政区划_青白江</th>\n",
       "      <th>行政区划_青羊</th>\n",
       "      <th>行政区划_高新</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100977</td>\n",
       "      <td>新津</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100978</td>\n",
       "      <td>新津</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100979</td>\n",
       "      <td>新津</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100980</td>\n",
       "      <td>新津</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100981</td>\n",
       "      <td>新津</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100982 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       region  lab_orientation  lab_elevator  lab_purposes  \\\n",
       "0          成华               53             1             6   \n",
       "1          成华               53             2             6   \n",
       "2          成华               53             2             6   \n",
       "3          成华               53             2             6   \n",
       "4          成华               53             2             6   \n",
       "...       ...              ...           ...           ...   \n",
       "100977     新津               68             1             6   \n",
       "100978     新津              101             1             6   \n",
       "100979     新津               29             2             6   \n",
       "100980     新津                0             1             6   \n",
       "100981     新津               29             2             6   \n",
       "\n",
       "        lab_house_structure  lab_decoration  type_room_num  type_hall_num  \\\n",
       "0                         7               2              3              2   \n",
       "1                         3               5              2              1   \n",
       "2                         7               4              3              2   \n",
       "3                         7               5              2              1   \n",
       "4                         7               4              2              2   \n",
       "...                     ...             ...            ...            ...   \n",
       "100977                    3               4              3              2   \n",
       "100978                    2               4              2              1   \n",
       "100979                    6               4              1              1   \n",
       "100980                    2               4              4              2   \n",
       "100981                    7               0              4              2   \n",
       "\n",
       "        type_wash_num  行政区划_双流  ...  行政区划_郫都  行政区划_都江堰  行政区划_金堂  行政区划_金牛  \\\n",
       "0                   1        0  ...        0         0        0        0   \n",
       "1                   1        0  ...        0         0        0        0   \n",
       "2                   1        0  ...        0         0        0        0   \n",
       "3                   1        0  ...        0         0        0        0   \n",
       "4                   1        0  ...        0         0        0        0   \n",
       "...               ...      ...  ...      ...       ...      ...      ...   \n",
       "100977              2        0  ...        0         0        0        0   \n",
       "100978              1        0  ...        0         0        0        0   \n",
       "100979              1        0  ...        0         0        0        0   \n",
       "100980              2        0  ...        0         0        0        0   \n",
       "100981              2        0  ...        0         0        0        0   \n",
       "\n",
       "        行政区划_锦江  行政区划_青白江  行政区划_青羊  行政区划_高新  行政区划_高新西  行政区划_龙泉驿  \n",
       "0             0         0        0        0         0         0  \n",
       "1             0         0        0        0         0         0  \n",
       "2             0         0        0        0         0         0  \n",
       "3             0         0        0        0         0         0  \n",
       "4             0         0        0        0         0         0  \n",
       "...         ...       ...      ...      ...       ...       ...  \n",
       "100977        0         0        0        0         0         0  \n",
       "100978        0         0        0        0         0         0  \n",
       "100979        0         0        0        0         0         0  \n",
       "100980        0         0        0        0         0         0  \n",
       "100981        0         0        0        0         0         0  \n",
       "\n",
       "[100982 rows x 32 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 单独处理行政区划\n",
    "#使用one-hot编码修改特征\"region\"\n",
    "import re\n",
    "df['region'] = df['region'].apply(lambda x: re.sub(r\"\\[|\\]|'\", '', x).split(',')[0])\n",
    "district = pd.get_dummies(df['region'], prefix='行政区划')\n",
    "data = pd.concat([df, district], axis=1)\n",
    "fin_data = data.copy()\n",
    "data.drop(['unit_price','price','title','floor','construction_area','from_url','idi','image_urls','release_date','lat','lng','community_name','type','orientation','elevator', 'purposes', 'house_structure','decoration'], axis=1, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除旧特征\n",
    "fin_data.drop(['title','from_url','idi','region','image_urls','release_date','lat','lng','community_name','type','orientation','elevator', 'purposes', 'house_structure','decoration'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = fin_data.corr()\n",
    " \n",
    "f, ax = plt.subplots(figsize=(13, 10))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# k=10\n",
    "# cols = corrmat.nlargest(k, 'price')['price'].index\n",
    "# cm = np.corrcoef(fin_data[cols].values.T)\n",
    "# sns.set(font_scale=1.25)\n",
    "# hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_features = fin_data.select_dtypes(include=['int64','float64','int32']).copy()\n",
    "num_features.drop(['price','unit_price'],axis=1,inplace=True)\n",
    "num_feature_names = list(num_features.columns)\n",
    "\n",
    "num_features_data = pd.melt(fin_data, value_vars=num_feature_names)\n",
    "g = sns.FacetGrid(num_features_data, col=\"variable\",  col_wrap=5, sharex=False, sharey=False)\n",
    "g = g.map(sns.distplot, \"value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Skew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>construction_area</td>\n",
       "      <td>3.288533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>type_wash_num</td>\n",
       "      <td>1.721692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>type_room_num</td>\n",
       "      <td>0.444548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_orientation</td>\n",
       "      <td>0.080359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>type_hall_num</td>\n",
       "      <td>-0.327500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>floor</td>\n",
       "      <td>-0.476538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_decoration</td>\n",
       "      <td>-0.956232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_elevator</td>\n",
       "      <td>-0.990491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_house_structure</td>\n",
       "      <td>-1.342497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_purposes</td>\n",
       "      <td>-2.441383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Skew\n",
       "construction_area    3.288533\n",
       "type_wash_num        1.721692\n",
       "type_room_num        0.444548\n",
       "lab_orientation      0.080359\n",
       "type_hall_num       -0.327500\n",
       "floor               -0.476538\n",
       "lab_decoration      -0.956232\n",
       "lab_elevator        -0.990491\n",
       "lab_house_structure -1.342497\n",
       "lab_purposes        -2.441383"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算各数值型特征变量的偏度（skewness）\n",
    "skewed_feats = fin_data[num_feature_names].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\n",
    "skewness = pd.DataFrame({'Skew' :skewed_feats})\n",
    "skewness\n",
    "# skewness[skewness[\"Skew\"].abs()>0.75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "skew_cols = list(skewness[skewness[\"Skew\"].abs()>1].index)\n",
    "for col in skew_cols:\n",
    "    #fin_data[col] = boxcox1p(all_data[col], 0.15)                                  # 偏度超过阈值的特征做box-cox变换\n",
    "    fin_data[col] = np.log1p(fin_data[col])                                                  # 偏度超过阈值的特征对数变换\n",
    "    \n",
    "del num_features, num_feature_names, num_features_data, g, skewed_feats, col, skew_cols      # 清除临时变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100982 entries, 0 to 100981\n",
      "Data columns (total 35 columns):\n",
      "price                  100982 non-null float64\n",
      "unit_price             100982 non-null float64\n",
      "construction_area      100982 non-null float64\n",
      "floor                  100982 non-null int64\n",
      "lab_orientation        100982 non-null int32\n",
      "lab_elevator           100982 non-null int32\n",
      "lab_purposes           100982 non-null float64\n",
      "lab_house_structure    100982 non-null float64\n",
      "lab_decoration         100982 non-null int32\n",
      "type_room_num          100982 non-null int64\n",
      "type_hall_num          100982 non-null int64\n",
      "type_wash_num          100982 non-null float64\n",
      "行政区划_双流                100982 non-null uint8\n",
      "行政区划_大邑                100982 non-null uint8\n",
      "行政区划_天府新区              100982 non-null uint8\n",
      "行政区划_天府新区南区            100982 non-null uint8\n",
      "行政区划_崇州                100982 non-null uint8\n",
      "行政区划_彭州                100982 non-null uint8\n",
      "行政区划_成华                100982 non-null uint8\n",
      "行政区划_新津                100982 non-null uint8\n",
      "行政区划_新都                100982 non-null uint8\n",
      "行政区划_武侯                100982 non-null uint8\n",
      "行政区划_温江                100982 non-null uint8\n",
      "行政区划_简阳                100982 non-null uint8\n",
      "行政区划_蒲江                100982 non-null uint8\n",
      "行政区划_郫都                100982 non-null uint8\n",
      "行政区划_都江堰               100982 non-null uint8\n",
      "行政区划_金堂                100982 non-null uint8\n",
      "行政区划_金牛                100982 non-null uint8\n",
      "行政区划_锦江                100982 non-null uint8\n",
      "行政区划_青白江               100982 non-null uint8\n",
      "行政区划_青羊                100982 non-null uint8\n",
      "行政区划_高新                100982 non-null uint8\n",
      "行政区划_高新西               100982 non-null uint8\n",
      "行政区划_龙泉驿               100982 non-null uint8\n",
      "dtypes: float64(6), int32(3), int64(3), uint8(23)\n",
      "memory usage: 10.3 MB\n"
     ]
    }
   ],
   "source": [
    "fin_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#确定数据中的特征与标签\n",
    "fin_data.drop(['unit_price'], axis=1, inplace=True)\n",
    "x = fin_data.as_matrix()[:,1:]\n",
    "y = fin_data.as_matrix()[:,0].reshape(-1,1)\n",
    " \n",
    "#数据分割，随机采样25%作为测试样本，其余作为训练样本\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=40, test_size=0.25)\n",
    "\n",
    "#数据标准化处理(归一化)\n",
    "ss_x = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "x_train = ss_x.fit_transform(x_train)\n",
    "x_test = ss_x.transform(x_test)\n",
    "y_train = ss_y.fit_transform(y_train)\n",
    "y_test = ss_y.transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征选择-（我们此处不需要）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.001, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=None,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征的选择--基于特征重要性图来选择:\n",
    "from sklearn.linear_model import Lasso##运用算法来进行训练集的得到特征的重要性，特征选择的一个作用是，wrapper基础模型\n",
    "lasso_model=Lasso(alpha=0.001)\n",
    "lasso_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>construction_area</td>\n",
       "      <td>0.70055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_高新</td>\n",
       "      <td>0.19788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_青羊</td>\n",
       "      <td>0.16280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_锦江</td>\n",
       "      <td>0.12236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_成华</td>\n",
       "      <td>0.11678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>type_room_num</td>\n",
       "      <td>0.10659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_武侯</td>\n",
       "      <td>0.10374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_天府新区</td>\n",
       "      <td>0.10078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_elevator</td>\n",
       "      <td>0.09937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_金牛</td>\n",
       "      <td>0.09001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>type_wash_num</td>\n",
       "      <td>0.06776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>floor</td>\n",
       "      <td>0.05024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>type_hall_num</td>\n",
       "      <td>0.03800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_decoration</td>\n",
       "      <td>0.02482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_双流</td>\n",
       "      <td>0.01914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_house_structure</td>\n",
       "      <td>0.01728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_orientation</td>\n",
       "      <td>0.01560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_龙泉驿</td>\n",
       "      <td>-0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lab_purposes</td>\n",
       "      <td>-0.00083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_天府新区南区</td>\n",
       "      <td>-0.00915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_高新西</td>\n",
       "      <td>-0.01502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_新津</td>\n",
       "      <td>-0.02962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_蒲江</td>\n",
       "      <td>-0.03000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_温江</td>\n",
       "      <td>-0.03744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_新都</td>\n",
       "      <td>-0.04556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_简阳</td>\n",
       "      <td>-0.04580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_郫都</td>\n",
       "      <td>-0.04959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_彭州</td>\n",
       "      <td>-0.07434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_金堂</td>\n",
       "      <td>-0.07841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_都江堰</td>\n",
       "      <td>-0.08089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_大邑</td>\n",
       "      <td>-0.09577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_崇州</td>\n",
       "      <td>-0.11983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>行政区划_青白江</td>\n",
       "      <td>-0.16040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Feature Importance\n",
       "construction_area               0.70055\n",
       "行政区划_高新                         0.19788\n",
       "行政区划_青羊                         0.16280\n",
       "行政区划_锦江                         0.12236\n",
       "行政区划_成华                         0.11678\n",
       "type_room_num                   0.10659\n",
       "行政区划_武侯                         0.10374\n",
       "行政区划_天府新区                       0.10078\n",
       "lab_elevator                    0.09937\n",
       "行政区划_金牛                         0.09001\n",
       "type_wash_num                   0.06776\n",
       "floor                           0.05024\n",
       "type_hall_num                   0.03800\n",
       "lab_decoration                  0.02482\n",
       "行政区划_双流                         0.01914\n",
       "lab_house_structure             0.01728\n",
       "lab_orientation                 0.01560\n",
       "行政区划_龙泉驿                       -0.00000\n",
       "lab_purposes                   -0.00083\n",
       "行政区划_天府新区南区                    -0.00915\n",
       "行政区划_高新西                       -0.01502\n",
       "行政区划_新津                        -0.02962\n",
       "行政区划_蒲江                        -0.03000\n",
       "行政区划_温江                        -0.03744\n",
       "行政区划_新都                        -0.04556\n",
       "行政区划_简阳                        -0.04580\n",
       "行政区划_郫都                        -0.04959\n",
       "行政区划_彭州                        -0.07434\n",
       "行政区划_金堂                        -0.07841\n",
       "行政区划_都江堰                       -0.08089\n",
       "行政区划_大邑                        -0.09577\n",
       "行政区划_崇州                        -0.11983\n",
       "行政区划_青白江                       -0.16040"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 索引和重要性做成dataframe形式\n",
    "FI_lasso = pd.DataFrame({\"Feature Importance\":lasso_model.coef_}, index=fin_data.drop([\"price\"],axis=1).columns) \n",
    "# 由高到低进行排序\n",
    "FI_lasso.sort_values(\"Feature Importance\",ascending=False).round(5)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(33, 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 获取重要程度大于0的系数指标\n",
    "FI_lasso[FI_lasso[\"Feature Importance\"] !=0 ].sort_values(\"Feature Importance\").plot(kind=\"barh\",figsize=(12,40), color='g')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()                     ##画图显示\n",
    "\n",
    "FI_index = FI_lasso.index\n",
    "FI_val = FI_lasso[\"Feature Importance\"].values\n",
    "FI_lasso = pd.DataFrame(FI_val, columns = ['Feature Importance'], index = FI_index)\n",
    "display(FI_lasso.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose_cols = FI_lasso.index.tolist()\n",
    "# choose_cols.append(\"price\")\n",
    "# choose_data = fin_data[choose_cols].copy()\n",
    "# del fin_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 建立模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, KFold\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.svm import SVR, LinearSVR\n",
    "from sklearn.linear_model import ElasticNet, SGDRegressor, BayesianRidge\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import math\n",
    "def get_mse(records_real, records_predict):\n",
    "    # 均方误差 估计值与真值 偏差\n",
    "    if len(records_real) == len(records_predict):\n",
    "        return sum([(x - y) ** 2 for x, y in zip(records_real, records_predict)]) / len(records_real)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def get_rmse(records_real, records_predict):\n",
    "    # 均方根误差：是均方误差的算术平方根\n",
    "    mse = get_mse(records_real, records_predict)\n",
    "    if mse:\n",
    "        return math.sqrt(mse)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "#定义交叉验证的策略，以及评估函数\n",
    "def rmse_cv(model,X,y):\n",
    "    # 针对各折数据集的测试结果的均方根误差\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, y, scoring=\"neg_mean_squared_error\", cv=5))   # cv 代表数据划分的KFold折数\n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 搜索各个算法的超参数\n",
    "# 定义先验参数网格搜索验证方法\n",
    "class grid():\n",
    "    def __init__(self,model):\n",
    "        self.model = model\n",
    "    \n",
    "    def grid_get(self,X,y,param_grid):\n",
    "        grid_search = GridSearchCV(self.model,param_grid,cv=5, scoring=\"neg_mean_squared_error\")\n",
    "        grid_search.fit(X,y)\n",
    "        # 打印最佳参数及对应的评估指标\n",
    "        print(grid_search.best_params_, np.sqrt(-grid_search.best_score_))\n",
    "        grid_search.cv_results_['mean_test_score'] = np.sqrt(-grid_search.cv_results_['mean_test_score'])\n",
    "        \n",
    "        # 打印单独的各参数组合参数及对应的评估指标\n",
    "        print(pd.DataFrame(grid_search.cv_results_)[['params','mean_test_score','std_test_score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 主成分分析\n",
    "pca_model = PCA(n_components=33)\n",
    "x_train = pca_model.fit_transform(x_train)\n",
    "y_train = pca_model.transform(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K近邻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8491326261885698\n"
     ]
    }
   ],
   "source": [
    "# r2\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn = KNeighborsRegressor()\n",
    "knn.fit(x_train,y_train)\n",
    "y_pre_knn = knn.predict(x_test)\n",
    "knn_score = r2_score(y_test,y_pre_knn)\n",
    "print(knn_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.14934448]\n"
     ]
    }
   ],
   "source": [
    "# 均方误差\n",
    "knn_score = get_mse(y_test,y_pre_knn)\n",
    "print(knn_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.386451138583249\n"
     ]
    }
   ],
   "source": [
    "# # 均方根误差\n",
    "knn_score = get_rmse(y_test,y_pre_knn)\n",
    "print(knn_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.38884963 0.39050593 0.38762811 0.38286215 0.39189377]\n"
     ]
    }
   ],
   "source": [
    "# 交叉验证\n",
    "print(rmse_cv(knn,x_train,y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8326275747742252\n"
     ]
    }
   ],
   "source": [
    "linear = LinearRegression()\n",
    "linear.fit(x_train,y_train)\n",
    "y_pre_linear = linear.predict(x_test)\n",
    "linear_score=r2_score(y_test,y_pre_linear)\n",
    "print(linear_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8326348338600317\n"
     ]
    }
   ],
   "source": [
    "# lasso = Lasso(alpha=0.0004,random_state=1,max_iter=10000)\n",
    "# lasso.fit(x_train,y_train)\n",
    "# y_pre_lasso = lasso.predict(x_test)\n",
    "# lasso_score=r2_score(y_test,y_pre_lasso)\n",
    "# print(lasso_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 集成模型算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.0004, 'max_iter': 10000, 'random_state': 1} 0.40570708106223147\n",
      "                                              params  mean_test_score  \\\n",
      "0  {'alpha': 0.0004, 'max_iter': 10000, 'random_s...         0.405707   \n",
      "1  {'alpha': 0.0005, 'max_iter': 10000, 'random_s...         0.405710   \n",
      "2  {'alpha': 0.0006, 'max_iter': 10000, 'random_s...         0.405714   \n",
      "3  {'alpha': 0.0007, 'max_iter': 10000, 'random_s...         0.405717   \n",
      "4  {'alpha': 0.0008, 'max_iter': 10000, 'random_s...         0.405722   \n",
      "5  {'alpha': 0.0009, 'max_iter': 10000, 'random_s...         0.405727   \n",
      "\n",
      "   std_test_score  \n",
      "0        0.002711  \n",
      "1        0.002712  \n",
      "2        0.002713  \n",
      "3        0.002713  \n",
      "4        0.002714  \n",
      "5        0.002714  \n"
     ]
    }
   ],
   "source": [
    "param_grid = {'alpha': [0.0004,0.0005,0.0006,0.0007,0.0008,0.0009],'max_iter':[10000],'random_state':[1]}\n",
    "grid(Lasso()).grid_get(x_train, y_train, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Ridge(岭回归)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 35} 0.4057021785303043\n",
      "          params  mean_test_score  std_test_score\n",
      "0  {'alpha': 35}         0.405702        0.002707\n",
      "1  {'alpha': 40}         0.405702        0.002707\n",
      "2  {'alpha': 45}         0.405703        0.002707\n",
      "3  {'alpha': 50}         0.405703        0.002707\n",
      "4  {'alpha': 55}         0.405703        0.002707\n",
      "5  {'alpha': 60}         0.405703        0.002708\n",
      "6  {'alpha': 65}         0.405704        0.002708\n",
      "7  {'alpha': 70}         0.405704        0.002708\n",
      "8  {'alpha': 80}         0.405705        0.002708\n",
      "9  {'alpha': 90}         0.405706        0.002708\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'alpha':[35,40,45,50,55,60,65,70,80,90]}\n",
    "grid(Ridge()).grid_get(x_train, y_train, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.SVR(支持向量回归)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C':[11,12,13,14,15],'kernel':[\"rbf\"],\"gamma\":[0.0003,0.0004],\"epsilon\":[0.008,0.009]}\n",
    "grid(SVR()).grid_get(x_train, y_train, param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.ElasticNet回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'alpha':[0.0005,0.0008,0.004,0.005],'l1_ratio':[0.08,0.1,0.3,0.5,0.7],'max_iter':[10000],'random_state':[3]}\n",
    "grid(ElasticNet()).grid_get(x_train, y_train, param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'min_child_weight':[4,5], 'gamma':[i/10.0 for i in range(3,6)],  'subsample':[i/10.0 for i in range(6,11)],\n",
    "'colsample_bytree':[i/10.0 for i in range(6,11)], 'max_depth': [2,3,4]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBRegressor(objective ='reg:squarederror')\n",
    "grid(xgb).grid_get(x_train, y_train, params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 根据网格交叉验证结果指定各算法的超参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#指定每一个算法的参数\n",
    "lasso = Lasso(alpha=0.0004,random_state=1,max_iter=10000)\n",
    "ridge = Ridge(alpha=35)\n",
    "svr = SVR(gamma= 0.0004,kernel='rbf',C=14,epsilon=0.009)\n",
    "# ker = KernelRidge(alpha=0.4 ,kernel='polynomial',degree=3 , coef0=1.2)\n",
    "ela = ElasticNet(alpha=0.004,l1_ratio=0.08,random_state=3,max_iter=10000)\n",
    "bay = BayesianRidge()\n",
    "xgb = XGBRegressor(colsample_bytree=0.4603, gamma=0.0468,learning_rate=0.05, max_depth=3,\n",
    "                   min_child_weight=1.7817, n_estimators=2200,reg_alpha=0.4640, \n",
    "                   reg_lambda=0.8571,subsample=0.5213, silent=1,random_state =7, nthread = -1)\n",
    "lgbm = LGBMRegressor(objective='regression',num_leaves=5,learning_rate=0.05, n_estimators=700,max_bin = 55,\n",
    "                     bagging_fraction = 0.8,bagging_freq = 5, feature_fraction = 0.25,feature_fraction_seed=9, \n",
    "                     bagging_seed=9,min_data_in_leaf = 6, min_sum_hessian_in_leaf = 11)\n",
    "GBR = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,max_depth=4, max_features='sqrt',\n",
    "                                min_samples_leaf=15, min_samples_split=10, loss='huber', random_state =5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current model is ElasticNet(alpha=0.004, copy_X=True, fit_intercept=True, l1_ratio=0.08,\n",
      "           max_iter=10000, normalize=False, positive=False, precompute=False,\n",
      "           random_state=3, selection='cyclic', tol=0.0001, warm_start=False),rmse: 0.832595294722317\n",
      "current model is SVR(C=14, cache_size=200, coef0=0.0, degree=3, epsilon=0.009, gamma=0.0004,\n",
      "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False),rmse: 0.8650937001121479\n",
      "current model is BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,\n",
      "              fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\n",
      "              normalize=False, tol=0.001, verbose=False),rmse: 0.8326267353040837\n",
      "current model is Lasso(alpha=0.0004, copy_X=True, fit_intercept=True, max_iter=10000,\n",
      "      normalize=False, positive=False, precompute=False, random_state=1,\n",
      "      selection='cyclic', tol=0.0001, warm_start=False),rmse: 0.8326348338600317\n",
      "current model is Ridge(alpha=35, copy_X=True, fit_intercept=True, max_iter=None, normalize=False,\n",
      "      random_state=None, solver='auto', tol=0.001),rmse: 0.8326255691279565\n",
      "current model is XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "             colsample_bynode=1, colsample_bytree=0.4603, gamma=0.0468,\n",
      "             importance_type='gain', learning_rate=0.05, max_delta_step=0,\n",
      "             max_depth=3, min_child_weight=1.7817, missing=None,\n",
      "             n_estimators=2200, n_jobs=1, nthread=-1, objective='reg:linear',\n",
      "             random_state=7, reg_alpha=0.464, reg_lambda=0.8571,\n",
      "             scale_pos_weight=1, seed=None, silent=1, subsample=0.5213,\n",
      "             verbosity=1),rmse: 0.8847698632651947\n",
      "current model is LGBMRegressor(bagging_fraction=0.8, bagging_freq=5, bagging_seed=9,\n",
      "              boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
      "              feature_fraction=0.25, feature_fraction_seed=9,\n",
      "              importance_type='split', learning_rate=0.05, max_bin=55,\n",
      "              max_depth=-1, min_child_samples=20, min_child_weight=0.001,\n",
      "              min_data_in_leaf=6, min_split_gain=0.0,\n",
      "              min_sum_hessian_in_leaf=11, n_estimators=700, n_jobs=-1,\n",
      "              num_leaves=5, objective='regression', random_state=None,\n",
      "              reg_alpha=0.0, reg_lambda=0.0, silent=True, subsample=1.0,\n",
      "              subsample_for_bin=200000, subsample_freq=0),rmse: 0.866229854835114\n",
      "current model is GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "                          learning_rate=0.05, loss='huber', max_depth=4,\n",
      "                          max_features='sqrt', max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=15, min_samples_split=10,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=3000,\n",
      "                          n_iter_no_change=None, presort='auto', random_state=5,\n",
      "                          subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
      "                          verbose=0, warm_start=False),rmse: 0.8899227194057208\n",
      "Optimal model is: GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
      "                          learning_rate=0.05, loss='huber', max_depth=4,\n",
      "                          max_features='sqrt', max_leaf_nodes=None,\n",
      "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                          min_samples_leaf=15, min_samples_split=10,\n",
      "                          min_weight_fraction_leaf=0.0, n_estimators=3000,\n",
      "                          n_iter_no_change=None, presort='auto', random_state=5,\n",
      "                          subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
      "                          verbose=0, warm_start=False) , score is : 0.8899227194057208\n"
     ]
    }
   ],
   "source": [
    "score = []\n",
    "models = [ela,svr,bay,lasso,ridge,xgb,lgbm,GBR]\n",
    "for regre in models:\n",
    "    regre.fit(x_train,y_train)\n",
    "    y_pre_regre = regre.predict(x_test)\n",
    "    regre_score=r2_score(y_test,y_pre_regre)\n",
    "    score.append(regre_score)\n",
    "    print('current model is {},rmse: {}'.format(regre,regre_score))\n",
    "print('Optimal model is: {} , score is : {}'.format(models[score.index(max(score))],max(score)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class stacking(BaseEstimator, RegressorMixin, TransformerMixin):\n",
    "    ##=============== 参数说明 ================##\n",
    "    # mod --- 堆叠过程的第一层中的算法\n",
    "    # meta_model --- 堆叠过程的第二层中的算法，也称次学习器\n",
    "    \n",
    "    def __init__(self,mod,meta_model):\n",
    "        self.mod = mod                                                # 首层学习器模型\n",
    "        self.meta_model = meta_model                                  # 次学习器模型\n",
    "        self.kf = KFold(n_splits=5, random_state=42, shuffle=True)    # 这就是堆叠的最大特征进行了几折的划分\n",
    "    \n",
    "    ## 训练函数\n",
    "    def fit(self,X,y):\n",
    "        self.saved_model = [list() for i in self.mod]          # self.saved_model包含所有第一层学习器\n",
    "        oof_train = np.zeros((X.shape[0], len(self.mod)))      # 维度：训练样本数量*模型数量，训练集的首层预测值\n",
    "        \n",
    "        for i,model in enumerate(self.mod):                    #返回的是索引和模型本身\n",
    "            for train_index, val_index in self.kf.split(X,y):  #返回的是数据分割成分（训练集和验证集对应元素）的索引\n",
    "                renew_model = clone(model)                     #模型的复制\n",
    "                renew_model.fit(X[train_index], y[train_index])           #对分割出来的训练集数据进行训练\n",
    "                self.saved_model[i].append(renew_model)                   #把模型添加进去\n",
    "                #oof_train[val_index,i] = renew_model.predict(X[val_index]).reshape(-1,1) #用来预测验证集数据\n",
    "                \n",
    "                val_prediction = renew_model.predict(X[val_index]).reshape(-1,1)    # 验证集的预测结果，注：结果是没有索引的\n",
    "                \n",
    "                for temp_index in range(val_prediction.shape[0]):\n",
    "                    oof_train[val_index[temp_index],i] = val_prediction[temp_index] #用来预测验证集数据的目标值\n",
    "                \n",
    "        self.meta_model.fit(oof_train,y)                       # 次学习器模型训练，这里只是用到了首层预测值作为特征\n",
    "        return self\n",
    "    \n",
    "    ## 预测函数\n",
    "    def predict(self,X):\n",
    "        whole_test = np.column_stack([np.column_stack(model.predict(X) for model in single_model).mean(axis=1) \n",
    "                                      for single_model in self.saved_model])        #得到的是整个测试集的首层预测值\n",
    "        return self.meta_model.predict(whole_test)            # 返回次学习器模型对整个测试集的首层预测值特征的最终预测结果              \n",
    "    \n",
    "    ## 获取首层学习结果的堆叠特征\n",
    "    def get_oof(self,X,y,test_X):                 \n",
    "        oof = np.zeros((X.shape[0],len(self.mod)))                #初始化为0\n",
    "        test_single = np.zeros((test_X.shape[0],5))               #初始化为0 \n",
    "        #display(test_single.shape)\n",
    "        test_mean = np.zeros((test_X.shape[0],len(self.mod)))\n",
    "        for i,model in enumerate(self.mod):                       #i是模型\n",
    "            for j, (train_index,val_index) in enumerate(self.kf.split(X,y)):          #j是所有划分好的的数据\n",
    "                clone_model = clone(model)                                            #克隆模块，相当于把模型复制一下\n",
    "                clone_model.fit(X[train_index],y[train_index])                        #把分割好的数据进行训练\n",
    "                \n",
    "                val_prediction = clone_model.predict(X[val_index]).reshape(-1,1)      # 验证集的预测结果，注：结果是没有索引的\n",
    "                for temp_index in range(val_prediction.shape[0]):\n",
    "                    oof[val_index[temp_index],i] = val_prediction[temp_index]         #用来预测验证集数据\n",
    "                    \n",
    "                #oof[val_index,i] = clone_model.predict(X[val_index]).reshape(-1,1)    #对验证集进行预测\n",
    "                # test_single[:,j] = clone_model.predict(test_X).reshape(-1,1)           #对测试集进行预测\n",
    "                \n",
    "                test_prediction = clone_model.predict(test_X).reshape(-1,1)           #对测试集进行预测\n",
    "                \n",
    "                # display(test_prediction.shape)\n",
    "                test_single[:,j] = test_prediction[:,0]\n",
    "            test_mean[:,i] = test_single.mean(axis=1)                                  #测试集算好均值\n",
    "        return oof, test_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stacking模型定义及初步评估\n",
    "# stack_model = stacking(mod=[ela,svr,bay,lasso,ridge,ker], meta_model=ker)\n",
    "\n",
    "# x_train = Imputer().fit_transform(x_train)\n",
    "# y_train = Imputer().fit_transform(y_train.reshape(-1,1)).ravel()\n",
    "\n",
    "# score = rmse_cv(stack_model,x_train, y_train)                 # 训练过程的评估指标\n",
    "# display(score.mean())"
   ]
  }
 ],
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